To Study Effects of Muscle Energy Technique and Proprioceptive Neuromuscular Facilitation on Computer Users Suffering from Neck Pain : A Comparative Study
Bibliographic record
Abstract
Prolonged use of computers during daily work activities and recreation is often cited as a cause of neck pain. Neck pain and computer users are clearly connected due to extended periods of sitting in a certain position with no breaks to stretch the neck muscles. Pro-longed computer use with neck bent forward, will cause the anterior neck muscles to gradually get shorter and tighter, while the muscles in the back of neck will grow longer and weaker. These changes will lead to development of neck pain. METHODOLOGY: A total 40 subjects were selected for study. They were divided into 2 groups 20 in each. Group A was given Muscle Energy Technique, And Group B was given Proprioceptive Neuromuscular Facilitation .Treatment was given 5 days per week, for 6 weeks. Outcome measure in form of NPRS ,And NDI were recorded on 1st day before treatment and after 6 weeks. RESULT: Group A and B showed significant improvement in all three outcome measures within group (P>0.05). Between Group A and B were significant (p>0.05). So, three groups were shows significant difference. CONCLUSIONS: The results of this Comparative study indicated that the treatment in all three Groups (Muscle Energy Technique And Proprioceptive Neuromuscular Facilitation) are effective in participants with Computer Users Suffering From neck pain on pain and functional disability. However, MET was found to be superior to Proprioceptive Neuromuscular Facilitation alone in participants with Computer Users Suffering From neck pain.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".